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Bayesian wavelength selection in multicomponent analysis

Brown, PJ; Vannucci, M; Fearn, T; (1998) Bayesian wavelength selection in multicomponent analysis. J CHEMOMETR , 12 (3) 173 - 182.

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Multicomponent analysis attempts to simultaneously predict the ingredients of a mixture. If near-infrared spectroscopy provides the predictor variables, then modern scanning instruments may offer absorbances at a very large number of wavelengths. Although it is perfectly possible to use whole spectrum methods (e.g. PLS, ridge and principal component regression), for a number of reasons it is often desirable to select a small number of wavelengths from which to construct the prediction equation relating absorbances to composition. This paper considers wavelength selection with a view to using the chosen wavelengths to simultaneously predict the compositional ingredients and is therefore an example of multivariate variable selection. It adopts a binary exclusion/inclusion latent variable formulation of selection and uses a Bayesian approach. Problems of search of the vast number of possible selected models are overcome by a Markov chain Monte Carlo sampling technique. (C) 1998 John Wiley & Sons, Ltd.

Type: Article
Title: Bayesian wavelength selection in multicomponent analysis
Keywords: multivariate regression, Bayesian wavelength selection, Markov chain Monte Carlo (MCMC), Metropolis algorithm, NIR spectroscopy, multicomponent analysis, selection bias, model averaging, VARIABLE SELECTION, GENETIC ALGORITHMS, REGRESSION, CALIBRATION
URI: http://discovery.ucl.ac.uk/id/eprint/151976
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